The qualitative analysis of the data shows that Obama often points at persons in the room with the hand that is on the same side as these persons in the room.

obama points with his left hand at people sitting in the left part of the room with respect to him and with his right hand at people sitting on his right side.


In the SWAT system for example, there is no support for user roles.

some tasks such as managing the number of annotators, monitoring the progress of the annotators, assigning tasks given to the annotators, and ensuring the quality of the submitted annotation are difficult to handle or manage with only one user type.


In Persian language, multitoken words can be written in three formats; completely separated by a space delimiter, separated by half-space 2 , or be attached to each other.

determining word and phrase boundaries is a complicated task in Persian.


Space correction based on learning To correct the spaces within multi-token words based on learning methods, a model was trained to find words with multiple parts separated by spaces.

we can take all parts of a word as one token.


Some generic tokenization methods can be used as a fallback, but this is rarely optimal.

such cases can only be solved by training a tokenizer on the provided input data, in order to apply the same tokenization choices in the thirdparty corpus.


The conversion to the IOB format is implemented in the following way: following the general good practice of preserving the form of the original data, the UD2 datasets contain annotations which indicate for every token whether a space follows the token or not.

8 it is possi-4 Last retrieved: 21/02/2018.


Moreover, even using a language-specific tokenizer might not always solve this problem, because some tokenization ambiguities cannot be solved other than by an arbitrary choice depending on interpretation; for instance, the documentation about the way tokenization is carried out for French in the UD2 corpus mentions that: "This tokenizing and segmentating choice is arbitrary and other French treebanks could choose to do otherwise."

18 a dataset-specific tokenizer should be trained on the provided input data in order to tokenize the third-party corpus accurately.


More generally, this approach is in line with the view that language scalability is one of the most important challenges in NLP tasks.

nLP tasks have to evolve into a more streamlined software engineering process across languages.


For example words (yktb, mktbp, ktAb) "he writes, library, book" have the same root (ktb) with the basic meaning of writing.

searching for any of these words by root, yields getting the other words which may not be desirable for many users.


Though some of these subtitles are indeed the "official" translation, many were translated or transcribed by amateur fans of the video content.

many of our translations are crowdsourced, and there are no guarantees on the fluency of the participants.


The relative subjectivity of certain attributes was treated as evenly as possible by considering the information provided by Face Place, if available.

for instance, properties related to ethnic type (black, Asian etc.)


Original framework Goal express thanks, compliment, excuse, ask a favour, ask information, ask advice, admonish, inform, remember, persuade, order, allow, invite Topic business, literature, domestic affairs, love, death, news, religion/ethics Revised framework Goal prompt for action, honour, help, inform, keeping contact, ask for reply Topic political work, literary work, current events, social circle A first observation is that the number of categories for goal and topic (see Table 1) is large and the boundaries of the categories are not always clear.

a new division is proposed with a smaller number of categories, where several closely related categories have been grouped together.


We repeated the construction of the simplified corpus and, subsequently, updated the core vocabulary accordingly.

despite vocabulary restrictions, our corpus achieved high quality in grammaticality and meaning preservation.


This vocabulary size also includes words such as proper nouns and symbols (238 words).

the vocabulary size of the simplified side is more than 2,000 words.


From the above observations, we see that parts which could not be covered by a word unit replacement existed significantly in the simplification.

it was necessary to simplify phrase units and sentence units depending on the circumstances.


Furthermore, there are no large-scale simplified data equivalents to the Simple English Wikipedia; as a result, no attempt has been made to construct a simplification corpus.

we used crowdsourcing to construct a Japanese simplified corpus containing 34,300 sentence pairs (Katsuta and Yamamoto, 2018).


Compared to evaluations of the crowdsourced corpus, evaluations in this paper show better results in meaning preservation and inter-annotator agreement.

the simplified corpus in this paper is higher quality than the simplified corpus constructed using crowdsourcing.


The prerequisite of these kinds of solutions is high-quality marked-up corpora, which are hard to acquire.

at present, solving CCWS for specific ancient books or a specific period of time is the main trend (Shi, Li and Chen, 2010).


They are accompanied by the written counterpart: the transcription either from their provider or made by us.

different principles applied in their transcription.


Such irregular organization and structural variety across sources pose a challenge to automatically extract critical information from the DUG documents.

there is a need for an annotated corpus for DUG documents.


The Romanian language is an under-resourced language, regarding resources available for bioNLP.

considerable efforts are carried on in order to improve the availability of the Romanian biomedical resources usable for bioNLP.


For classifying humor in texts, it is important to understand the semantics of the sentence.

we took a three word window as a feature to train our classification models to incorporate the contextual information.


Paintings are usually labeled with the title, the artist, and the style of painting, but they are not categorized for the emotions they evoke.

automatically detecting the emotions evoked by art is of considerable importance.


All of this means that an open-ended question asking annotators to enter the emotions evoked through a text box is sub-optimal.

we chose to provide a set of options (each corresponding to a closely related emotions set) and asked annotators to check all emotions that apply.


When aggregating the responses to obtain the full set of emotion labels for an item, we wanted to include not just the predominant emotion, but all others that apply, even if their presence is more subtle.

we chose a somewhat generous aggregation criteria: if at least 40% of the responses (four out of ten people) indicate that a certain emotion applies, then that label is chosen.


8 With our annotations, we wanted to quantify the impact titles have on the emotional response elicited by the art.

as indicated earlier, we asked annotators to provide the emotions evoked by the image alone, the title alone,  Table 6: The percentage of annotations that have exactly the same emotion sets selected for image and art, title and art, or image and title by the same annotator.


As with direct representations of laughter, we hypothesize that the length of the repetition may correlate with degree of emphasis or intensity of underlying emotion.

we want to return the length of the repetition as well as the underlying word in its canonical spelling.


The problems we address here are often deeply rooted in national legal systems world-wide and reflect fundamental economic as well as social concerns (Mittelstadt and Floridi, 2016).

they are rather persist even over long periods of time and NLP research has to find ways to accommodate to the overarching legal ecosystems.


We therefore propose to substitute authentic clinical data by synthetic documents written by medical practitioners for education purposes.

we are dealing with artificial, yet plausible and realistic medical scenarios, rather than identifiable social individuals and their personal legacy data and are thus able to circumvent any privacy concerns.


This way, the problem of getting clearance from third-party ethical and legal bodies to deal with clinical documents addressing real individuals can be avoided.

of carefully reviewing available educational material for the German language, we came up with a list of ten textbooks containing suitable synthetic data: (Siekmann and Irlenbusch, 2012), (Siekmann and Klima, 2013), and (Siekmann et al., 2016) supply reports of orthopedics and trauma surgeries, including fictional administrative information, such as patient numbers, time and date information, as well as the name of the physician in charge.


Such substitutes can easily be extracted from electronically published educational medical textbooks.

privacy protection concerns do not arise since artificial actors rather than real-life individuals are in focus.


6 Copyright exists if originality is found in the selection or arrangement of the content, i.e., the "intellectual creation" has to be found in the database structure.

copyright in databases protects only the structure and does not extend to the content.


In the present case, the trained model lacks sufficient originality to qualify as a derivative as well as an independent work.

it is not a work of authorship for copyright law purposes.


Most of the texts that can be found online do not carry any licence at all or are part of commercial offers which do not permit reproduction.

in the present scenario we investigate if such texts can still be used for training models.


Inc., Trustees of the University of Pennsylvania and/or other original authors."

the licensing status of the individual texts is not entirely transparent, and should be prudently considered to be under an "all rights reserved" status.


Accordingly, there will be shorter extracts that meet such a condition, and longer extracts that do not meet it.

it can be argued that current EU law permits the temporary copy of non-licensed copyright works for purposes such as "data capturing" as long as the cumulative conditions of Art.


While the test to be applied is not 11 vs. 3 consecutive words, but that of the "author's own intellectual creation", it seems plausible that three consecutive words are too insubstantial to constitute a "reproduction in part" of the original corpora.

the trained model does not reproduce in part the original corpora.


Technically speaking, there is no direct reproduction of the sentences in the original language.

it should not come as a surprise that the right of translation was the first right to be included in the minimum standard of protection in the oldest copyright international treaty, the Berne Convention.


Ideally the infrastructure is populated with as many resources as possible.

continued efforts are being put into gathering what resources there are and curating them, targeting also the many resources that have been created in the context of research projects but have not (yet) found their way to one of the data centres or repositories.


The permitted values are given in CLARIN's Concept Registry 15 but, in CLARIN context, these are not (or rather: no longer) considered restrictive.

we added extra values for some of the metadata elements.


Moreover, the information should not only be stored in Excel format, but also be made accessible in CMDI metadata files.

we decided to look for an interface which allowed editing hierarchical metadata in a CLARIN offers a versatile and well documented metadata editor for CMDI profiles: COMEDI 20 (Lyse et al., 2014).


Note that in TCF part-of-speech tags appear in a separate layer referring to token objects in the tokens layer, while in LIF, part-ofspeech is given as the value of the pos feature.

9 in this case conversion requires either expanding one LIF view into two TCF layers, or collapsing two TCF layers into one LIF view.


Primary data is stored in the TCF text element, but in most cases no anchors into the text are provided.

conversion from LIF to TCF requires mapping character offset anchors to each token element, and conversion in the reverse direction demands recomputing offsets from the primary source.


TCF, on the other hand, represents phrase structure implicitly by exploiting the hierarchical nesting of XML tags to represent parent-child relations among constituents using embedding XML elements; only when an element's (node's) children consist entirely of tokens are they listed explicitly within a tokenIDs element.

conversion of phrase structure trees in TCF to LIF requires interpreting and flattening the XML structure.


Internally, a given tool may use different terminology; the only necessity is that the tool is wrapped to map the exchange vocabulary into the internal terminology and vice versa.

in principle, a single mapping of a tool's specific terminology into and out of the common exchange vocabulary is sufficient to enable information exchange with all others.


(6) The updated lexicon would again provided latest information to fluid segmentation & tagging in next session.

a cycle was established where not only the flexibility of linguistic pattern is assured, but annotators' effort cumulated in the process.


This also includes the AuthorityManager, which enables the management of resource access rights at the level of individual users or groups of them, thus enabling privacy and data security.

by being based on UIMA DI, the AuthorityManager avoids Bottleneck (d).


For instance, the data may contain misalignment issues, formatting issues, encoding corruption issues, sentence breaking issues, etc.

the Tilde MT platform performs data filtering before MT system training.


In our previous research, we identified that NMT systems are sensitive to systematic noise that can be found in the parallel data (Pinnis et al., 2017a).

additionally to the filters used for SMT systems, for NMT systems Tilde MT performs also the following filtering steps to ensure that the parallel corpora are filtered more strictly (Pinnis et al., 2017c): 1.


Tilde MT supports NMT models that use linguistic input features (Sennrich and Haddow, 2016).

the source side can be factored using language-specific factorisation tools (depending on the source language -either part-of-speech or morphological taggers or syntactic parsers).


While rule systems provide an high degree of interpretability, they may require a great deal of development effort to obtain reasonable accuracy.

rule-based components represent substantial barriers for both quality control and internationalization.


Following Yarowsky (1997), we use equivalence classes for context tokens classified as instances of specific semiotic classes such as numbers, currencies, measure expressions, or letter sequences; see Ebden and Sproat (2014) for a full list.

the featural representation of "1993 to present" and "2017 to present" are identical, since 1993 and 2017 are both instances of the DATE semiotic class.


It would have been a waste of human labor to have such simple and predictable variations annotated manually.

only pairs of sentences that differ sufficiently from each other are accepted into the development and test sets.


However, e 1 occurs aligned against f 1 only once, whereas e 2 21 times.

p (f 1 |e 1 ) is high (=1), because e 1 is always translated as f 1 .


Different from these, the SPADE provides annotations of gold parse trees by a linguistic expert and gold phrase alignments identified by three annotators.

20, 276 phrases are extracted from 201 sentential paraphrases, on which 15, 721 alignments are obtained that at least one annotator regarded as paraphrases.


We asked a linguistic expert with rich experience on annotating HPSG trees to annotate gold-trees to paraphrasal sentence pairs.

201 paraphrased pairs with gold-trees (containing 20, 276 phrases) were obtained.


The meanings of "beat" and "battled" are similar, and play the same syntactic and discourse role in the structure of the texts.

the substitution of "beat" for "battled" fulfills the formal requirements of a "same polarity substitution".


Moreover, the structure of scales intrinsically provides shifting.

while something being good allows it to be even more positive ("The movie was good.


As described in the previous section, this approach cannot guarantee quotidian connotational meanings from texts because of the reporting bias.

it is important to construct knowledge bases of such commonsense knowledge by manual intervention as well as through automatic acquisition.


Unlike majority voting, this method calculates the probability based on worker agreements.

9,073 sentences remained out of 11,189 representative sentences.


For each antecedent candidate, the five types of vectors were concatenated.

according to the dimension numbers of feature change vectors and word2vec vectors described above,vectors in FC, W2V, and BOTH are 693 dimensional, 5,500 dimensional, and 6,193 dimensional vectors respectively.


In this experiment, we regarded anaphora resolution as an antecedent candidate ranking task and compare three conditions (the case using only the feature change information, the case using only the word2vec information, and the case using both of the feature change information and the word2vec information).

it was shown that the condition where only the feature change information was used outperformed the other conditions.


An individual's position on an issue is not simply a binary support-or-oppose stance on the issue, but rather a cumulative sum of many nuanced beliefs and opinions.

assertions are a useful means of capturing one's position on a controversial issue.


From the agreement judgments, we created the agreement matrix AM , which contains one column per assertion, and one row per participant.

each cell ad p,a in this matrix has the judgment provided by participant p for assertion a. ad a is the vector of all judgments provided for assertion a, and ad p is the vector of all judgments provided by participant p. To obtain consistent and comparable fine-grained scores indicating the degree of support or opposition from multiple respondents, we used a technique known as Best-Worst Scaling (BWS) (Louviere et al., 2015b).


Assertions expressed by participants provide key insights into why an issue is controversial, what aspects of the issue people are particularly passionate about, etc.

organizing the assertions by amount of agreement (to quickly view the assertions with most and least agreement) and strength of support or opposition is particularly useful.


A polarization score of 1 indicates that -on averagean equal number of participants agree and disagree with the assertions on an issue.

a polarization score of 0.5 describes an issue in which more and less polarizing assertions keep a balance.


In the example it is clear that a factual event is reported, but a set of emotions like sadness and anger could be triggered on readers because of the news content.

we considered that news articles are a good source of emotional reactions, specially those published in Twitter.


We considered that free access allows more users to read the articles, so more and better responses can be available.

we discarded newspapers that required a paid subscription.


According to their experience when tagging emotions, it was easier for annotators to have an extensive list of labels than only the six ones defined in level 1 of each group: love, joy, surprise, anger, sadness and fear.

we obtained a fine-grained annotated dataset.


It is worth mentioning that percentage values of emotions are independent because a news article may provoke 100% of joy and 70% of anger at the same time.

the sum of these values do not necessarily sum 100%.


It has been reported that these incidents have not only created mental and psychological agony to the users of the web but has in fact forced people to deactivate their accounts and in rare instances also commit suicides.

the incidents of aggression and unratified verbal behaviour has not remained just a minor nuisance but has acquired the form of a major criminal activity that affects a large number of people.


While annotating, the annotators were given the following instructions (in addition to a detailed annotation guidelines describing the different tags, with examples) - • Annotators were allowed to mark more than one discursive effects if it seem that certain tweets / comments may be annotated for more than one discursive effect.

the annotators were asked to choose ALL the discursive effects that a comment depicts.


However, General Nonthreatening aggression can also contain abuse and if it does, it should be marked so.

ntAG comments can be only abuse in addition to itself and nothing else.


The reason is, however, simply the conventions applied in the FGDVT to cases where the surface expression is location rather than (= in place of) a (true) addressee (as an animate agent); typically, this happens for offices (to the clerk.ADDR vs. at the court office.LOC), government seats (to the governor.ADDR vs. in Annapolis.LOC), etc.

in all these cases, functors from the valency lexicons and the deep dependencies can easily be mapped to a common set of SRs, taken from FrameNet in this case.


If it is not present, this construction simply says that there was an offer while not specifying anything more specific about it, unless the word offer or its equivalent is left out and the Entity Offered is specified as a dependent directly on the verb extend itself, as in the banks ... extended [company] up to $90 million in revolving loans.

both the PAT as well as the restrictive attribute of the PAT (denoted as PAT(RSTR) can be the Entity Offered.


This is an example where the translated text probably does not have the same effect on the target reader: the translator simply added content to a sentence based on her/his understanding of the context.

the verb place was, at present, not included in the OFFER synonym class.


This indicates that a large portion of the domainspecific vocabulary that we want to capture in our model consists of multi-word entities.

we should take this into account during the training of embeddings.


2008;Rapp 2008), might not be the optimal choice.

1 http://cl.haifa.ac.il/projects/mwn/index.shtml Liebeskind et al.


1999;Pantel and Ravichandran 2004) and Lin's mutual information metric (Lin 1998)).

each term in the corpus is a potential candidate term.


The texts are tokenized and tagged with all possible morphological analyses.

we directly extracted all the three possible term representations from the analyzed texts.


For example, the related terms: mišhw (someone), xi (living thing) and adm (person) for the target term ab (father), or the related terms: p`wlh (action) and m`šh (deed) for the target term ktivh (writing).

our measured recall was expected to be low.


We focus upon that simple, coarse-grained level of etymological relatedness, focusing upon clusters of lemmas that would be verbalized into the same verbal lemma.

"appealing" is clustered with "appeal" we do not dive into distantly related terms with the same root, such as "appellation" or "compel."


The recordings were made in the municipality of Boarnsterhim in central Fryslân (see Figure  1), because the inhabitants-more than other Frisiansadvocated the monolinguistic use of Frisian (Feitsma 1989  In 1990, the population consisted of 17,710 people and slightly increased every year.

of another division boundary alteration in 2014, Boarnsterhim was again divided and combined with four other municipalities.


Bybee 2006, Phillips 2006, Diessel 2007.

it is crucial to offer frequency information to the end user.


Too, another form of AA script transliterated with Latin characters called "Arabizi transliteration" used heavily on the internet and SMS (Cotterell et al., 2014;Al-Badrashiny et al., 2014;Bies et al., 2014).


Considering the variety of the many recordings, controlling the salient features would not have been possible.

of not controlling the linguistic features within the single samples, the difficulty of locating a specific sample might also have differed depending on the amount of salient features of a dialect that appeared in a sample, especially in the case of lexical or certain phonological cues.


This means that not only knowledge of current, but also of older dialects determined the performance of the player.

age might become a more important factor, as the older players might still have had contact with those older stages of the dialect.


At the same time, information that is not precise enough might render an analysis more difficult.

one has to balance between not getting enough information if it is too personal, and not getting the relevant data if it is too general.


Although NMT has been rapidly developed in recent years (Kalchbrenner and Blunsom, 2013;Sutskever et al., 2014;Bahdanau et al., 2015;Tu et al., 2016), Chinese-Portuguese MT has not received much at-tention using NMT because training data are not readily enough.

the performance is still low using these state-of-the-art approaches.


Therefore these articles are mostly comparable rather than parallel texts (paragraph alignment tasks); 3) Chinese texts are usually written in chronicle style, while its corresponding Portuguese texts written in several sentences.

these sentences are not one-to-one aligned (sentence alignment tasks).


As shown in Figure 1, there is usually a language switch button on the top of a web page which can be used to extract its corresponding page in the other language.

we extract these useful links and align documents based on this heuristic rule.


While Google search does perform better than WordsEye on realistic sentences (average rating of 1.871 -between "completely correct" and "mostly correct"), performance breaks down when faced with imaginative sentences (average rating of 3.825 -between "partially correct" and "mostly incorrect").

we have shown that WordsEye is superior for imaginative sentences, and Google search is superior for realistic sentences.


The signal quality and the spontaneous degree have consequences on the human annotation time (Bazillon et al., 2008).

two strategies can also be conceivable: 1. annotating various documents; 2. annotating specific documents.


To solve this problem, the recording of the user screen is manually segmented into one or several actions thanks to the log file.

the measured duration accurately correspond to the actual actions.


Although the experimental result of the SICK corpus rejects the null hypothesis, the result of the SNLI corpus does not reject it.

the SNLI corpus has a hidden bias to allow prediction of TE labels from hypothesis sentences even if no context information is given by a premise sentence.


These results indicate that the null hypothesis is rejected for the SICK corpus, but it is not rejected for the SNLI corpus.

hypothesis sentences of the SNLI corpus have a hidden bias to allow prediction of their TE labels without premise sentences.


In the second model, two LSTMs are sequentially connected.

it is possible to consider that the memory cells of these LSTMs are directly modeling a recognition process unlike the parallel LSTM model.


The main difference to our approach is that we explicitly represent senses both in clusters and in their hypernym labels, which enables us to connect our sense clusters into a global taxonomic structure.

we are the first to use semantic classes to improve hypernymy extraction.


Note also that, the bestunpruned model (t = 0) has BabelNet hpc-avg of 0.279, which is only 10% lower than the best model, yet the unpruned model has an order of magnitude larger vocabulary and a more fine-grained representation (734 vs. 1,870 clusters).

if coverage is important, the unpruned model is recommended.


Traditional relation extraction methods focussing on intra-sentence relation extraction will fail to extract the relation book/author/works written, between the entities (Ayan Rand, The Fountainhead), (Ayn Rand, Atlas Shrugged), (Alisa Zinovyevna Rosenbaum, The Fountainhead), (Alisa Zinovyevna Rosenbaum, Atlas Shrugged) from these two sentences.

in order to extract these relationships, both sentences must be considered collectively.


Since then the dataset has been extensively used for evaluation purposes (Riedel et al., 2010;Surdeanu et al., 2012).

given the usefulness of the dataset developed by Mintz et al.


Our dataset is created following the distant supervision approach.

it remains unclear how accurate the annotations produced by following distant supervision.


Naturally, high occurrence of words with diacritics does not imply that generating diacritics is an ambiguous task.

we also evaluate word error rate of a simple dictionary baseline to diacritics restoration: according to a large raw text corpora we construct a dictionary of the most frequent variant with diacritics for a given word without diacritics, and use the dictionary to perform the diacritics restoration.


As shown in Table 3, the proposed uninames method recognizes all characters the uninorms method does, and some additional ones.

we employ the uninames method to strip diacritics in the paper.


(2016) showed that concatenating all these language-specific sets for training yields best results.

we used only concatenated files for training the models for each of three languages in our experiments.


However, such testing sets contain mainly standard language and are thus worse for comparison of models aiming to generate diacritics for non-standard language.

we plan to create additional development and testing sets in the future work.


Our goal in this project is to contribute to component technology development for multimodal dialogue systems.

our target is to construct dialogue corpora; more specifically, those are not of human-human dialogues but of human-system dialogues.


For the purpose of privacy protection, several fundamental rules for treating personal data have been established by the Japanese government.

the following issues should be addressed prior to sharing the corpus, even for research purposes.


This study concentrates only on the driver-relevant states.

four emotional states occurring frequently while driving a car were defined.


The characteristic features of the data collected for these states is assumed to be very similar.

the algorithms will not be able to differentiate between them.


Moreover, when we turn to other datasets with annotated emotion information such as data crawled from Twitter (Mohammad and Bravo-Marquez, 2017), the labeled units (posts or sentences) are independent.

models built with these datasets lack the ability to consider contextual information essential * These authors contributed equally to this work.


After this step, however, we still found named entities like lowercase or foreign names and emails.

we manually checked and cleaned utterances in the first step again to prevent the accidental reveal of personal data.


We first embed the words to the word embedding , which is publicly available 300-dimensional GloVe pre-trained on Common Crawl data (Pennington et al., 2014).

each utterance in u i is represented by a feature matrix F ∈ R M ×300 .


In a paragraph, the sentiment of each utterance is dependent on the context.

within a dialogue, there is a high probability of inter-dependency with respect to their sentimental clues.


The goal of the project is to deliver a prototype system applicable in practice, i.e., supporting the moderation of current online discussions.

a connection to the production forum system is required, such that the prototype works with the live stream of new postings in near realtime.


No enterprise would tolerate the risks involved with a prototype system directly manipulating its production databases for a service used by thousands of users every day.

the data was mirrored to a dedicated database server for the prototype system, such that the production system is shielded from potential bugs.


Previous theoretical work identifies lists of functions (see e.g., Abdul-Zahra (2010)), and recent work in NLP picks specific functions and aims to identify them in large collections of CS texts (Begum et al., 2016;.

such studies use different levels of analysis, and often confuse observations (such as observing code-switched reiteration or translation) with CS functions (reiteration is often used for emphasis), as in the following example from Begum et al.


1) shows diglossia, where one speaker is capable of speaking two languages, but does not mix them in the same conversation, also outside of our definition of CS.

they are shown with a black background in Fig.


This renders it difficult for readers to understand the arguments, let alone evaluate them.

comment 1 is much more desirable for readers, whether it be government agencies or fellow citizens.


Rebuttals are counterarguments which directly challenge the truth of a statement.

they have been conceived as arguments which support the negation of the proposition functioning as claim.


The argumentation structure of the example shown in Figure 3 has an UNDERCUT relation.

the dependency structure of the argument scheme shown in Figure  5(a) is equivalent to the argument structure in Figure 3 except for the UNDERCUT relation, which maintains the same source and changes the target to the source of its target relation (i.e., 2 → (1 → 5) is changed to 2 → 1).


216ff; the original algorithm was later implemented and then tested on the whole of PDT and the results were reported in Hajičová et al., 2005, see also Rysová et al., 2015).

we had at our disposal the total of 150 dependency trees with marked (binary) division into Topic and Focus and with the annotation of coreference and basic bridging relations between referring expressions of the adjacent sentences.


Human evaluators who oversaw the quality of generated sentences revealed that the use of POS tags has increased the fluency of language and rare word translation performance by a significant amount.

we can empirically prove that the use of POS tags improves the quality of synthetic training data, which in turn reduces the rare word problem in NMT.


That is, each word begins with a 'd' sound and it contains a consonant cluster 'nt'.

it is highly probable that the ancestral word contained a form resembling 'd ... nt ...' (Fortson, 2004, 3).


(2011) show that there is an asymmetry between feature economy in which vowel inventories tend to be more economical than consonant inventories.

the articulatory and perceptual constraints that may govern the changes in phonological inventories over time must be incorporated into models of the evolution of spoken languages.


However, for low-resource languages with very little data, these alignments may be extremely noisy or may not exist at all.

improving the quality of word alignments leads to more accurate phrase pairs, which in turn improves the quality of an SMT system (Och and Ney, 2003;Fraser and Marcu, 2006).


Furthermore, a goal of AMR is to provide consistent semantic representation despite language-specific syntactic idiosyncracies.

representing the meanings associated with fully syntactic patterns required a novel annotation approach.


8 For example, if one says I was too tired to drive, context may or may not make clear whether the driving actually took place.

this is one case in which practical considerations of what can be captured consistently outweighed the desire for a deeper representation.


To test COUNTER in a realistic setting, we cannot compare the DRSs to themselves or to a DRS of the translation, since those are too similar.

the 19,000 English sentences of the DRS are parsed by an existing AMR parser (van Noord and Bos, 2017) and subsequently converted into a DRS by a rule-based system, AMR2DRS, as motivated by Bos (2016).


Given the amount of time that can be saved by re-using existing work, there is a tendency to see the expansion approach favoured over the merge approachit also lends itself extremely well to the automatic construction of synsets, where input from lexicographers is minimal to zero.

the research described in this paper (and particularly in section 3.)


Whereas some signs are produced using only one hand, others are normally produced using two hands (and in such cases, there are some dependencies with regard to what happens on the two hands, as discussed in Section 3.4).

we introduce an additional parameter related to this distinction.


Although we use a smaller set of signs, 350 total, we get the correct match 93.3% (top-1) and 97.6% (top-5) of the time.

our recognition accuracy compares favorably with the two approaches just mentioned, and furthermore has the advantage of being scalable.


Over the last few years, it has been convincingly shown the CNNs can produce rich representation of the input image by embedding it to a fixed-length vector, such that this representation can be used by a variety of vision tasks (Sermanet et al., 2013).

it is natural to use CNN as image encoder, In specific, CNN is first pre-trained for classification.


For example, the Movielens 20M dataset (Harper and Konstan, 2016), which provides tag assignments between ≈27K movies and ≈1,100 unique tags also suffers from these problems.

a fine-grained tagset and their assignment to movie plots can help to overcome these obstacles.


The goal is to predict tags from the written movie plots.

relevant tags are those that capture properties of movie plots (e.g.


The need for immediacy in computer-mediated communication, its interactive nature, as well as the space constraints posed by the medium used, often result in a fragmentary writing (Martínez-Alonso et al., 2016) and very concise utterances, where more or less meaningful portions of a sentence are omitted 6 .

and in a way that recalls the so-called headlinese, a given sentence may have function words removed, such as: Given the preference of UD scheme in assigning headedness to content words, no solving strategy has been necessary for such cases.


Unlike in tweet 6, where the author appears to have made plans to drive to Yosemite Valley the day after tweeting, in tweet 7 it is not clear  if the author is simply stating a wish.

annotators chose UNK for all temporal tags.


Regression using a ANOVA RBF kernel did not yield any usable results in experiment 2.

the positive results reported by Zhang and Rudnicky (2001) could not be replicated in our setting.


Apart from @lemma and @lemmaRef, up till now TEI encoders could only resort to using the generic attribute @ana for inline linguistic annotation, or to the quite complex system of feature structures for robust linguistic annotation, the latter requiring relatively complex processing even for the most basic types of linguistic features.

there now exists a small set of basic descriptive devices which have been made available at the cost of only very small changes to the TEI tagset.


The result of ATECC is usually an ordered list of potentially equivalent candidate terms in the target language, for each candidate term in the source language.

the evaluation needs to include an evaluation of both term extraction, and term linking.


ATECC, which involves not only a term recognition module, but also a multilingual term linking module is even more difficult to evaluate.

several corpora in three different languages and domains were manually annotated.


In general, such information is rarely available.

we assume a GEF community manager to contact the WebLicht developers and asks them for support.


The morphology level including multi-word expressions is encapsulated within a bunsetsu.

we can concentrate on the annotation of purely syntactic phenomena.


The absence of an overt sense subject does not mean that the adjective is not used in the predicate.

we regard both adjectives as clauses, and we assign acl.


It was decided to interpret subjects in the same way in Świgra, which means new rules had to be added for those realisations.

much fewer verbs are inherently subjectless in this new interpretation.


It should be mentioned that patterns do not require a particular word order, only particular dependents.

the sentence in Figure 5 is a match as well.


• If a candidate sentence contains three or more coordinate clauses and some of them are copular clauses, due to the conversion rules the output sentence will contain different types of ellipsis.

the output structure is not parallel-dependent clauses do not repeat the structure of the main clause.


Obviously, the rules described above filter out a substantial amount of sentences.

we currently do not check prepositions in English; instead, we manually fix sentences where prepositions are not compatible.


To correct these errors, we noted that for each part-ofspeech within a language in Wiktionary, authors use only a handful of distinct table layouts.

it was sufficient for a human to verify and correct a single lemma parse from a particular layout, and apply those corrections to all similar lemma parses.


In practice, these inflectional 'chunks' are treated as portmanteau morphemes that combine not only subject person and number, but also the voicevalence markers found in position 1 of the verbal template given above.

a lexical entry for a verb tagged as taking the Ø-imperfective paradigm, which has no preceding prefixes, and has an *l-voice-valence marker (cf.


Furthermore, we suspect that it is possible to abstract away from concrete words and infer the missing morphological information based only on morphological and syntactic properties of the context.

we formulate a hypothesis, that based purely on the morphosyntactic information present in other words in the sentence (i.e.


Since the true tags are available neither in automatic nor manual annotation, we select 34 Polish words which are often abbreviated 4.1., look up their inflected forms in the Polimorf morphological dictionary (Wolinski et al., 2012), and gather sentences which contain at least one of these words.

we obtain sentences where an abbreviation might have been possible, and consequently acquire labels (the true morphosyntactic tags) for a supervised machine learning algorithm.


Note that these data sets only contain the publications themselves, typically in PDF format.

using such data sets for citation-based task evaluation is troublesome, since one must preprocess the data (i.e., (1) extract the content without introducing too much noise, (2) build global identifiers for cited papers, and (3) annotate citations with those identifiers.)


Indeed, mathematical expressions rather disturb the generic learning of machine learning models.

we ignore math constructs and similar content for our needs.


Even if XML-based formats are emerging, the majority of scientific articles are still accessed as PDF files.

effective tools to extract structured textual content from PDF files represent a key technology to enable scientific text mining (Ronzano and Saggion, 2016).


One main issue was the standardizing of data.

we have developed algorithms and a unified data model.


of vocal annotation (performed by Nikolay Korotaev and Vera Podlesskaya) transcripts were obtained, in the form of text documents, as well as textgrid files with multi-layer annotation prepared with the help of speech analyzing program Praat (fon.hum.uva.nl/praat) and reflecting temporal anchoring of all annotated phenomena.


With parallel corpora similarly annotated for discourse phenomena, (a) we may be able to avoid some of the differences arising from annotation efforts carried out separately for each language, (b) help discourse-level corpus annotation efforts to improve our understanding of discourse structure, and in turn (c) help to enhance language technology applications.

our aim in developing TED-MDB is to investigate the phenomena surrounding discourse relations on the basis of texts belonging to the same genre by annotating them similarly across languages.


In such cases, we annotate the arguments in the usual way and assign the label NoRel to the relation (see so in example 7 above).

we tease apart discourse connectives from their discourse marker use in the speeches.


At present, the analyses of discourse structure are mainly concentrated on the micro level, while the analyses on macro level are few.

this paper focuses on the construction of representation schema and corpus resources on the macro level of discourse structure.


The overall discourse structure is relevant to the discourse genre and discourse pattern.

discourse structures vary if the genres are different.


Among overlaps, we considered interruptions whenever the interrupted speaker was not able complete the sentence.

there might be other interruptions in the data that do not follow overlaps which, for the current analysis, were not taken into account.


We also experimented with the various representations for the speaker id that is concatenated with the respective utterances but could find no differences.

our proposed model uses minimal information for the context.


The visualization flexibility not only provides alternative views on given rhetorical structures, it also makes TREEANNOTA-TOR's output more "readable" to modern theoretical linguistics.

tREEANNOtAtOR may reach a new group of users.


Yet, under-resourced historical languages, such as Ancient Greek and Latin, often lack enough primary material for certain time periods to properly train machinelearning models (a.k.a., classifiers).

only basic NLP techniques (e.g., checking similarity thresholds over string-and n-gram-shingles), which are independent from an advanced global knowledge or training experience, are applicable for analyzing historical corpora (Büchler et al., 2010).


We are interested to learn if we can cross-apply models for paraphrastic reuse detection from English paraphrases to Latin text reuse.

we identify text features that are language independent.


(Finch et al., 2005) discuss some ambiguous characteristics of the MSRP corpus and give related examples.

when two sentences are annotated for semantic equivalence that does not necessarily require them to be parahrasaes of each other.


For these reasons, the above mentioned standard dictionary building methods cannot be used for our purposes.

it was necessary to conduct experiments with alternative methods.


Wikipedia has very valuable translation texts since these translations were manually made by editors.

it is quite surprising, that using Wikipedia title pairs as a dictionary proved to be just the third most precise method.


This test differentiates 35 items in order to characterize dysarthria.

the use of perception for the evaluation of dysarthric speech is frequent and well documented.


One of our goals is to evaluate the usefulness of crowdsourced NLU data.

we evaluate our German data set acquired from crowdsourcing against data collected by an experimental study, in order to gain a baseline of what constitutes naturalness in user input.


We propose different methods of how to evaluate usefulness and naturalness of crowdsourced data.

we create a baseline by means of collecting real system usage data.


For the purpose of this reasoning, the state-of-the-art, called CausalNet (Luo et al., 2016), harvests causality scores of cause-effect term pairs, e.g., ('broken','clean'), by mining their causal patterns, e.g., "If...broken ..., then... clean...", from an extremely large text corpus (10TB).

it achieved a remarkable accuracy (70.2%) from COPA.


However, it includes several components that the KR and CV communities have made significant progress on during the past few decades.

we provide an attractive list of solution techniques accessible enough for the communities to start making progress on the VCOPA task.


This design is intended to ensure that answering these questions requires both computer vision techniques and commonsense knowledge harvesting, and cannot be easily answered when using an individual technique.

we made 500 questions, each of which has one premise image and two alternative images.


In most projects focused on creating linguistic resources there exists a variety of different predictive (machine learning) models that could be used to pre-filter examples before human-annotation.

the question arises regarding how to choose the best model to aid in the annotation process.


However, in this paper we argue that, in contexts where annotation budgets are an important consideration and, hence, we can only afford to annotate a subset of our data, the sequence of the data becomes much more important than the application of the threshold.

in such a constrained setting, selecting a pre-filtering prediction model based on a measure of accuracy which evaluates the interplay of the scoring and thresholding process on an entire test set may not be the optimal approach.


Generally, different models will score instances differentally and will also have different thresholds.

they will return different proportions of positive and negative predictions for a given test set.


However, for the purposes of this discussion (and without loss of generality) we will assume that the interactions between the instance scoring and thresholding in each model is such that both models predict that two thirds of the test set are positive examples.

∼60% of our data is labeled as positive by our model (shaded green), while 40% is labeled as negative (shaded red).


Because we know that for a given project budget we can have a human annotator review a limited number of instances, we are interested not so much in which model is the best at identifying positive instances across a full test-set, but rather which model will sequence its output in such a way as to return the largest number of true positive predictions within the top n instances ranked by the model as positive.

accuracy is not an ideal measure in this situation, and relying exclusively on it can have very expensive consequences.


These values may seem small, but, as already stated in Section 2., these are calculations performed on a test set, and the proportions do scale to larger samples.

in larger projects with vast amounts of data and numerous annotators, saving 60% of the annotation budget can turn out to be quite a considerable amount.


In this example, the language model of the ASR system will not be able to identify the out-ofcontext error by only considering the current sentence.

developing a context component to enhance the text understanding is an important task to improve state-ofthe-art systems.


To evaluate the performance of the modification procedure, we apply the process on the 2016 TEDTalk corpus (Cettolo et al., 2012) and test multiple baseline systems against the dataset.

a standard sentence-based language model (LM) is evaluated on the corpus, reaching a F-score of 6.51%.


(2) Part-of-Speech Tagging For the semantic out-of-context substitutions, only certain part-of-speech (POS) classes are taken into account.

every token in the dataset needs to be assigned a POS category.


We show that the substituted tokens are difficult to infer by computational models that solely employ the local context.

a context-aware approach needs to be developed to enhance the performance on the task.


The results reported can only be taken as hints for the differences in performance between the possible configurations of the modules.

a qualitative error and disagreement analysis has been carried out in an attempt to elucidate these issues.


What makes the fact incorrect is that the parts do not fit together.

we use as a score the (log) probability of the whole fact minus that of each individual part.


can turn into, a serious problem) would be true in medieval times, or in chess commentary.

in an effort to come nearer the original task of recognizing impossible from plausible OIE extractions, we experiment with a small set of manually annotated facts.


They obviously have a temporal relationship over two consecutive sentences.

the relation should be RT2 instead of RT3 or RT4 due to the relation's range.


Another thing that we can observe from Table 3 is that systems ranked using Assessor B's judgments agree more strongly with the judgments of Assessors A and C than do Assessors A and C agree with each other.

we have some indication that choosing Assessor B's judgments would be reasonable for the six topics that had been judged by all three assessors.


During two different periods of time: 7 consecutive days in February 2014 and 2 consecutive days in January 2015, several TVBN shows from 8 different French channels have been recorded and web press articles appearing on the Google News homepage have been downloaded.

frNewsLink resources (whose elements are described in Section 3. and detailed statistics are given in Section 5.)


has been performed in order to obtain: 1topic segmentation annotations and 2-linking annotations between topic segments and press articles.

this corpus is very useful for many tasks such as topic segmentation, topic titling, video linking, semantic similarity, events follow-up and topic modeling (grouping documents addressing similar topics).


To this purpose table 3 provides figures in terms of number of words in the overall press articles sub-corpora.

of the linking annotation process, TVBN topic segments can be separated into two sets.


Since the resulting amount of tokens was pretty much the same we did not follow this path.

we observe that there is nothing compare to the time performance of spaCy and right now we can say the same about the PyRATA language expressiveness.


Second, the automatic processing of the scanned documents using image processing methods and subsequent natural language processing module will allow for faster and more efficient annotation of individual documents with metadata and will greatly simplify the possible assignment of similarity links among documents, which may be of different media nature (text, audio, or photographs).

the researchers will be able to use a much wider context of the recording.


The feature vector is composed of the differences between the mean values of color components of a pair of consecutive scans in two color spaces (HSV and RGB).


Whilst human languages differ profoundly in a number of parameters, the basic receptory and cognitive apparatus is essentially the same for all humans.

basic confusability patterns should across time and language be roughly stable.


Consequently, basic confusability patterns should across time and language be roughly stable.

we believe one can use psycholinguistically derived confusability information for a weighting regardless of the time period or language from which the textual material may stem.


(2004a) mention the exapmle of <cl> and <d>, it is unlikely that the reason for the confusion lie in any other modality than vision.

modelling each modality separately and summing them, apart from having neurological correlates, is not unreasonable but the presented approach is surely just a first step to investigate a complex and data sparse object.


In this paper, we review methods of textual representation from two ways: one by topic representation, obtaining vectors that encode the pertinence of words in sentences if they appear together in a corpus, and one by word embeddings, creating word representations based on their occurrence context and combining them in a single document vector.

the methods for textual representation investigated in this paper are: • Latent Semantic Indexing (LSI) (Landauer et al., 1998): This method uses the term-document matrix that encodes the frequency of each term by document and its eigenvalues and eigenvectors to decompose this data and find a new representation.


This paper uses two different ways to generate features from an image: hand-crafted descriptors, which are predefined ways to find regions of interest and encode them into vectors; and neural features, automatically extracted and selected by an ImageNet pre-trained neural network.

the methods for visual representation investigated in this paper are: • Scale-Invariant Feature Transform (SIFT) (Lowe, 2004): This method extracts features that are invariant to scale, illumination and rotation.


Response times can be divided with quantiles with a q selected for the desired granularity, and comparable moves should be those that occur in similar semantic contexts, that is, [m j−n ..m j+n ] where m j is the move in question, examined in a window of size 2n + 1.

p(t i |m j−n ..m j ..m j+n ) represents the probability that a response time t falls in an interval i given a move and surrounding context.


This allows evaluation to test the coverage of the system, but not necessarily how well the human adapts their behavior to the system.

trials might also be conducted after the user has been presented with written descriptions of possible gestures (as in Section 2.1.


), its morphosyntactic analysis with part-of-speech tags and morphological features, and its dependency tree are in Figure 2  prep -preposition fin -finite verb adj -adjective sg -singular number nom -nominative case gen -genitive case m1 -human masculine (virile) gender m3 -inanimate masculine gender f -feminine gender ter -third person imperf -imperfect aspect nwok -non-vocalic pos -positive degree (of adjectives) In order to test a system of image generation based on two descriptions, information whether these two descriptions are semantically related, or whether the meaning of one description entails the meaning of the other one, seems to be relevant.

each sentence pair is human-annotated for relatedness in meaning and entailment.


As far as we know, no emotional audiovisual corpus exists containing controlled, acted or natural anomalies to address specifically the anomaly detection question.

in this paper, we present a new and complementary multimedia corpus called EMOLY which contains human-centered anomalies.


Depending on the verb, none or up to four obligatory complements can be required.

there are five different verb valency classes.


The lowest precision is 62.0% and the highest value is 66.8%.

the yielded F-measure lies between 52.1% and 64.6%.


The relations between the search terms of the query language on the one hand and the database structure on the other hand are defined in an easy-to-edit configuration file in .yaml format.

search terms can be added or changed by the administrator by simply editing that configuration file.


Methodology Sentence boundary detection is often regarded as a solved task, at least in the domain of well-edited texts.

typical sentence boundary detectors focus on orthographic clues, as punctuation and capitalization marks.


When a definite noun appears in an object position, it is marked for the accusative case and the marker follows the definite marker.

segmenting the definite marker has an effect on the status of the case marker that behaves as a clitic.


In both Arabic and Hebrew, case markers are treated as morphological features whereas, in Amharic, they are independent syntactic elements.

we have 'case' relations rather than morphological features.


For instance, the guideline suggests that a VP tag is used for any verb including auxiliary and relative verbs attaching a preposition.

an auxiliary, other verbs, and relative verbs with a preposition have similar tags as VP.


Thus, an auxiliary, other verbs, and relative verbs with a preposition have similar tags as VP.

an expression tagged as VP following their tagsets, will have different syntactic structures, i.e.


it can be an auxiliary with a preposition or it is a verb or a relative verb with a preposition but tagged similarly.

the distinction they want to capture by the tags of the subcategories will not be used when such forms attach a preposition.


In addition, they modify the verb phrase and uniformly lack specification for most verbal grammatical features like tense, aspect, etc.

we consider them as non-main verbs and the final verb as a main verb.


Since, the light verb is semantically null, it cannot be the head.

in such a case, we have decided the copula to be treated as a main verb and the head of the phrase.


Some people tend to write in phonemic form (the abstract form or what one intends to say) and some tend to write in the phonetic form (what is actually uttered).

there are different forms for a given word in the corpus collected from most electronic media.


for every word of the input sentence, possible elementary trees are selected where the semantic information on the lemma of the word matches with the information on the anchor node of the tree.

the grammar used by TuLiPA, similarly to the XTAG grammar (XTAG Research Group, 2001), is composed of three elements: a lexicon of unanchored elementary trees (tree templates), a lexicon of lemmas and a lexicon of fully inflected forms.


Many industries including gaming, film, appliances, and accessibility (Szarkowska, 2011) now have vested interest in creating TTS voices.

there is a lot of needs to produce a variety of good quality voices in different languages.


The ability to experiment with many voice building algorithms is an important part of achieving high quality TTS voices for low-resourced languages.

voice Builder was designed with this in mind.


For the languages, new words appear as time passes, and the usage of existing words may change as well.

it is essential to maintain the dictionary continuously.


The words in NEologd are collected automatically or semiautomatically.

we screen the resource to exclude entities that are included by unclear reason, or unnecessary for our purposes.


In addition, the database creation mainly focuses on English text.

in other words, chemical compound information other than English is not good enough to be available.


It is difficult for us to recognize differences between chemical compounds in patent documents.

a high skill and a large amount of time are required to readers.


In this paper, we created only similar chemical compounds, created by deducting one substituent from the chemical compounds.

there may be many relevant chemical compounds that we can not find.


On the other hand, even if similar chemical compounds become different from original chemical compounds, users are still difficult to estimate overall structures.

we provide chemical formulas (Rational formula, Molecular formula) like Figure 5 to estimate overall structure.


The reason to make two different designs for Spanish and Chinese texts is because, although the texts are parallel, comparing with the Spanish texts, the Chinese texts are more difficult to understand because of the different meanings but the same word (including some annotated DMs 4 ).

we consider that, for a Chinese text, it is better to remove all the DMs and mix the correct answers to let the users to choose so that they can understand the text better by filling the DMs.


These activities include cooperative phases (Barkley et al., 2005;Morimoto and Otsuka, 2012): Together, students observe their practices and reflect on them after the observations.

far, many video annotation tools have been developed to observe such activities (Rich and Hannafin, 2009;Yousef et al., 2014) because recorded video and sound are useful tools, particularly for reflecting on practice.


According to an investigation conducted by the Japanese Ministry of Internal Affairs and Communications 2017, 96.8% of people in their 20s connect to the Internet on their smartphones.

it is feasible to use smartphones as devices for FWM in the classroom, in particular if a small number of auxiliary smartphones can be secured.


The video camera does not need to be controlled by FWM clients or a FWM server because the annotation data is synchronized with the video data, based on the time information related to these data.

generic video cameras such as video cameras for home use and smartphones can be used to record the video.


We assume that children converge on a grammar structured like the grammar for adults and actually almost identical to it.

it makes sense to classify children's utterances in terms of the adults' grammar, so that we can compare children's and adults' utterances.


It is not always a priori evident what these corrections should look like.

explicit conventions and guidelines have to be developed on what syntactic structure should be assigned to such utterances.


It is orthographically incorrect to write this contraction as das.


The example is a main clause, but we also want to find examples in subordinate clauses.

we specify that the properties of the top node of the parse tree containing the selected elements (smain, i.e.


This lack of data was caused by the difficulty that mothers verify the their children's vocabulary after their children comprehend a large number of words.

we set constraint f(x)≤f'(x) under which a', b', and c' should be obtained.


However, simultaneously obtaining a', b', c', a, b, and c under the constraint is mathematically difficult.

instead of that complicated constraint, we introduce a simple constraint, a≤a', in the following optimizing method: To estimate correctly the production rate, we introduce a constraint where the word comprehension rate should exceed the word production rate.


There are typically many ways how a correct response can be expressed, and the variety of responses is increased even more by orthographic and grammatical deviations, which occur frequently in student writings.

automatically scoring student writings usually involves various other tasks central to NLP such as spell checking, grammatical error correction, POS tagging, paraphrase recognition, textual entailment, or argument mining.


Therefore, learner answers can optionally be normalized before feature extraction or language errors are simply annotated to be consumed later by a feature extractor that extracts the number and type of errors in an answer as features.

we add spellchecking components based on spellcheckers like Jazzy 3 .


Real-life scoring scenarios often fall somewhere in between the supervised and the unsupervised case: some part of the data is typically unlabeled and should be labeled by the tool (and afterwards potentially re-checked by a teacher).

we assume that scenarios where a model is to be trained on existing labeled training data and applied on new unlabeled test-data will occur frequently; for example, when the same or similar prompts have already been used and scored in an earlier exam and are now administered to a new cohort of students.


This would allow us to train previous unavailable statistical tools for the processing of Galician texts.

of our effort, seven new linguistic processors for Galician are presented: a rule-based tokenizer and statistical tools for POS tagging, lemmatization, Named Entity Recognition, Named Entity Disambiguation, Wikification and graph-based Word Sense Disambiguation.


There are around 160 attested Auxiliary strings, each of which can in principle be inflected for the full range of nearly 200 endings found on main verbs.

including the possible Auxiliaries, each verb phrase can in principle be inflected for approximately 32,000 distinct forms.


Text is of course also essential for NLP, computational linguistics, and other research uses of corpora.

we consider machine-readable text a highly important element for any digitised text corpus.


For the dev sets of 8,000 and 5,000 words, the optimal heuristics were identical to those found in §4.1.

there is no change in projected results.


Information retrieval-based approach is supposed to work exceptionally well for short and well-defined sections, but it cannot be expected to work well on more freely formulated sections.

we also use Hidden Markov Models (HMM), which are supposed also to include infor-    mation about sections ordering into the decision.


The texts in the corpus were randomly sampled from the heterogeneous sources of the corpus and presented to the annotators without context.

we applied a guideline that focuses on text-level polarity, abstracting away from the (opaque) writer's intent.


In the latter cases, it is difficult to delineate the exact boundary because of its syntactic complexity.

we limited the syntactic unit of the annotation target.


For example, "脂肪肝 合併 2 型糖尿病" (type 2 diabetes complicated with fatty liver) is divisible into "type 2 diabetes" and "fatty liver".


Because this ratio can differ depending on the corpus, it does not mean directly that the coverage of disease names with reliable ICD code in J-MeDic is low.

j-MeDic mainly contributed to extension of the entry because disease names of reliability level C are useful to search disease names, although their ICD codes are not reliable.


Step 1: preprocessing In the first trial, we simply put the output of the morphological analyzer JUMAN into word2vec, however, the result was unsatisfactory.

we decided to utilize syntactic information for the input data.


we obtained 100 synonym expressions classified into10 clusters (10 clusters * 10 expressions).


We evaluated the expressions for each cluster by comparing them to sense descriptions of the compound verb in CVL.

59% of extracted words are evaluated as synonyms.


We compared the clusters to sense descriptions of the compound verb in CVL.

65% of extracted clusters are evaluated as representing the proper meaning of the compound verb.


It allows communication with pre-existing TMs and the collaborative TM MyMemory, terminological databases, concordance searches within the TMs and machine translation (MT) engines, from which the MT provider, also named MyMemory (a combination of Google Translate and Microsoft Translator), is freely available.

professional translators will not need to invest much time in learning how to use the tool and will benefit from the interoperability of CAT-related formats (TBX for glossaries, XLIFF for bilingual files, TMX for TMs, etc.).


Therefore, we only need a formal definition of how to annotate the different types of fake news and the values (degree of membership in a fake news type) associated with each of these annotations.

fANE defines several additional classes and relations (cf.


Apart from adding a new component to an analysis pipeline, no further changes must be required on the NLP side.

we separate the work of a language engineer, developing the NLP pipeline (e.g., to find domain-specific entities) from the work of a knowledge base engineer, who defines the structure of a concrete KB.


The export rules -how NLP results are mapped to triples -are themselves described in form of RDF triples using our new mapping language.

the configuration how a specific knowledge base has to be populated with triples can be read from the same knowledge base, which provides for an introspective NLP export (R3).


The LiDo RDF project evolved out of the initial aim to provide referenceable term and concept resources for the LiDo Terminological and Bibliographical Database (in short LiDo TBD).

the data that is available on the LiDo TBD website has been converted into the LiDo RDF dataset following the creation procedure as described in Section 4.


units of meaning) the latter are defined as languagespecific linguistic objects.

within the LiDo TBD source data a linguistic concept is unique and associated with a linguistic term that in turn hypostatizes the concept.


Due to the supported Sparqlification Mapping Language (SML) that is integrated in Sparqlify we could easily express the necessary PostgreSQL to RDF mappings with SML 19 .

the Sparqlify tool provides as output file the converted RDF data in N-Triples, a plain text serialization format for RDF graphs.


In order to enrich the LiDo RDF dataset with external data sources and simultaneously provide the possibility for other dataset creators to point to linguistic concepts or terms within LiDo RDF, an integration of the data into the LLOD Cloud is considered a worthwhile undertaking.

a manual set of 50 links adhering to the requirements of publishing datasets within the LLOD Cloud 23 has been created as a starting point to add LiDo RDF to the already existing collection of linguistic data within the Cloud.


While these are established de facto standards in the field, editing principles for philological corpora are only now emerging, with different alternative vocabularies (POWLA, NIF, TELIX) 10 currently being discussed.


As evident from the table, most of the data are of an administrative nature, but thus far untranslated.

we focus on this genre but aim to develop tools and resources to link with CDLI data in general.


SSPR performs better than any of the three individual parsers, with average scores of 83% LAS (labeled attachment score) and 88.44% UAS (unlabeled attachment score).

ssPR is in the same accuracy class with the top 5 performers at the CONLL 2017 dependency parsing shared task for Romanian.


Knowing what to expect from the document structure makes the handwritten recognition task a little bit easier to accomplish and enables automatic validation of data consistency.

we engaged, together with the medical experts from NIDNMD, into transforming the paper-based archive into a digital searchable archive.


We selected the value of those properties using SPARQL query and through the SPARQL endpoint of the related triple store.

those selecting property values are used to instantiate the resources (according to Section 3.3).


And even if a solution to this problem were found, it would be complicated and unintuitive to search for the components of the NP stem in such constructions.

the two components of the predicate NP are treated as two separate tokens, each being annotated with the part of speech of the underlying component.


The Finnish public administration has a pressing need to translate information into both Swedish and Finnish, as well as into major European languages in order to reach the multilingual population.

the public sector (e.g., Office of the Prime Minister, Municipality of Helsinki, Ministry of Justice, Tax Administration) is actively following the developments in CEF Automated Translation and would be potentially very interested in integrating the platform into public services.


Norway has a diverse linguistic landscape, due to the coexistence of several official languages: Norwegian variations Bokmål and Nynorsk, Sami, and Kven.

language resources exist in each of these distinct languagesthough, of course, in varying degrees of volume and coverage.


Collecting the right idioms was a hard task due to the lack of MWE categorization.

we carried out the idiom collection manually where we discarded all the entries that were semantically equivalent to their lexical definitions which means to be not non-compositional.


Therefore, this MWE is deductible from the meaning of each of its components, the meaning gets "to follow the book's writing".

it is not considered an idiom, in contrast of the idiom "out of the blue" which means "an event that occurs unexpectedly", the meaning of "blue" is "color" then no relationship exists between "blue" or "out of" with "unexpected happening".


It is not adopted in our scheme because a predicate which would have Uu has been annotated with an EVq or EVi tag in the previous phase.

we use the following six tags for factuality mark-up.


For example, 'pay a visit', the visit represents an event with two arguments (the visitor and the visitee) while the cake in make a cake represents a simple noun, without any eventive information.

the former one can continue to the next Test 2 is to investigate whether the noun is used in one of its original sense.


In contrast, 点 心 dianxin 'dessert' is a common NP which refers to a concrete entity and does not contain any predicative information.

the eventive information all comes from the verb 做 zuo 'do'.


The advice can be complemented by its author.

this one is excluded from light verb construction.


After that, we collect possible VMWE occurrences in Ontonotes and filter candidates with the help of gold dependency trees, then we formalize VMWE annotations as a multiword sense disambiguation problem to exploit crowdsourcing.

we annotate 7,833 VMWE instances belonging to various categories, such as phrasal verbs, light verb constructions, and semi-fixed VMWEs.


In many cases, a noun phrase follows this type of MWEs.

in a content-head scheme like Universal Dependency (McDonald et al., 2013), a verb of this MWE governs a head of the noun phrase, that is, such MWE does not form a subtree (Figure 1).


Also, we resolve pseudo overlaps originating from false annotations.

we reduce the number of overlaps to 11 instances, which correspond to essential overlaps, such as "look back" and "look .. on .. as" in the following sentence: "He may be able to look back on this election as the high-water mark of far-left opposition.".


As a result, we reduce the number of overlaps to 11 instances, which correspond to essential overlaps, such as "look back" and "look .. on .. as" in the following sentence: "He may be able to look back on this election as the high-water mark of far-left opposition.".

of annotations, we get 1,608 VMWE types and 7,833 instances in Ontonotes.


Concerning Basque phraseology, research has been done both to describe some linguistic phenomena and to develop NLP tools (Alegria et al., 2004;Gurrutxaga and Alegria, 2012), but researchers have had an almost exclusively monolingual perspective.

our aim is, on the one hand, to analyse how MWEs are translated, and, on the other hand, to propose a method to improve their computational treatment in bilingual tools.


The head of a CN is considered to open slots that are filled by specific conceptual categories (Rosario et al., 2002;Maguire et al., 2010) that play a semantic role.

the semantic category that is the head of the CN determines what can be done to it by means of the addition of modifiers that fill the slots opened by the head.


As for the CNs of sand, the semantic relations in 6 more CNs were clarified by means of ws CQL queries.

the relations in 15 CNs of sand were elicited by means of KPs (word sketches or ws CQL queries).


The results showed that using our processing methods can still produce a tagger with an f-measure score of 0.95 and higher.

we consider the suggested processing methods sufficient for this task.


Similar to the convolutional encoder, the self-attentive encoder does not explicitly capture information about the position of the input in the sequence.

we use the position-aware embeddings again.


The assumption is that a word vector is learned in such a way that it best predicts its surrounding words in a sentence or a document (Mikolov et al., 2013a;Mikolov et al., 2013b).

the words associated with a literal target will have larger projection onto a target σvn.


Furthermore, a meaningful extension must not just work on a technical level, but also sustain the advantages of taking workload from the user.

we conducted an analysis of common deep learning setups in the literature to learn about the challenges to reproducibility and convenience.


This format is quite popular and wide-spread as it allows a rather easy transformation of the textual data into an integer representation.

one The beautiful tree … DET ADJ NOUN ...


ual models have high variance when compared to the variance of ensemble models.

one might place lower confidence in their model when an ensemble approach is not used.


Their interest in the relatedness task is directly linked to the task of entity disambiguation.

their goal is learning to rank, which affects their definition of related terms.


In particular, each syllable is labeled by either segmentation tag B (Begin of a word) or I (Inside of a word).

our approach can be viewed as an extension to word segmentation of the automatic SCRDR approach for POS tagging (Nguyen et al., 2014;.


In the course of annotating the corpus, we came across phenomena that could not be adequately analyzed with the old version of the tagset.

we refined and extended it by introducing ten novel tags.


Selection of the other half has been guided by the wish to include as wide a variety of linguistic phenomena as possible in the gold standard corpus.

these sentences were manually selected and contain instances of rarer morphosyntactic phenomena.


In all, some 4000 noun bases, 4000 verb bases, 600 postbases, and 500 particles were newly integrated into the analyzer's lexicon.

when evaluated against the end-of-chapter translation exercises provided in the reference grammar, the morphological analyzer performed reasonably well, successfully parsing several hundreds of words of varying morphological complexity (see Examples 4-6).


For accusative languages, the corresponding default ranking would be nominative > accusative > other For languages with ergative alignment, the default ranking would be ergative > absolutive/nominative > dative > other This hierarchy is suggested to reflect the fact that the ergative case is almost exclusively used for subjects, constituents marked with nominative/absolutive can by subjects or direct objects, and the dative case can be used to mark subjects and both direct and indirect objects.

the hierarchies for both accussative and ergative languages correspond to the hierarchy for grammatical roles: subject > object > other So far, we discussed possible extensions of the current UniMorph schema that arise from our work on Caucasian and other low-resource languages.


We discussed a number of peculiarities of these languages and potential conceptual difficulties in the application of the UniMorph scheme to them and other languages.

we suggest the following extensions to the UniMorph schema: • human-readable labels for LGSPEC features, e.g., LGSPEC-TM instead of LGSPEC4 for Megrelian, 22 https://github.com/acoli-repo/unimorph • a ranking-based numerical scheme to represent iterative features in nominal inflection, • a ranking-based numerical scheme to encode multiple arguments of polyvalent verbs in head-marking languages, and • the postulation of a default ranking for verbal arguments, as well as the possibility to posit languagespecific rankings.


Furthermore, text-based methods are where difficulties are encountered the most (Kruger et al., 2005;Arimoto and Okanoya, 2016).

we targeted the effort to build an emotion analysis system focusing on text data.


In fact, in many cases, people may experience a mixture of various emotions at the same time (Choe et al., 2013).

the corpus for any emotion analysis task should be multilabel.


Human have developed language to fit their needs of expressing ideas and feelings.

when describing our emotion, we tend to use some specific words.


Because our goal is to predict the emotional labels for each utterance in a conversation, we also have to vectorize the previous utterance and the entire conversation to capture the contextual information .

the vector representation of an utterance is a 300-dimensional-vector  The two networks are both experimented on our annotated corpus.


On the other hand, customer loyalty has long been a topic of high interest in both academia and industry.

it is attractive to consider exploiting customer loyalty information by analyzing the voice of customer.


In addition, tweets include spelling errors, hashtags, user handles, retweets, short forms etc.

translation of such noisy texts becomes a difficult task.


Texts are short and include many references to concepts that have a specific terminology.

sA methods must be adapted to the syntactic and semantic features of financial microblogs.


This accuracy is the overall accuracy of the rule as determined by solely the rule-based classifier.

the integrator weights each word on the basis of how good a rule predicting it as a target was.


Note that we convert individual sentences into words.

the dataset in the case of book snippets has 6377 instances, while the one of tweets has 6610 instances.


Baseline 2: Sequence labeling has been reported for opinion target identification (Jin et al., 2009).

we use SVM-HMM (Altun et al., 2003) with default parameters as the second baseline.


This is because the ambiguity and noise of general feedback.

inter-annotator agreement and Kappa value is quite low compared to reflective prompt-based annotations.


However, unlike traditional reflective prompts, general feedback contains more useful information.

it is important to analyse general feedback.


We also left out all multi-word expressions and single-letter lemmas (typically corresponding to the names of letters of the alphabet, musical notes, or units of measurement).

only single-word adjectives, interjections, nouns, and verbs, having a lemma two letters or longer were sampled.


Furthermore, it has been extremely difficult to determine whether phonology plays a role in lexical access, as opposed to post-access; even if reliable priming effects from words to phonological neighbors are obtained, opponents of a phonological theory of word reading can still claim that these effects occur postaccess, and do not influence the actual access of lexical items.

little attention has been given to the exact featurization of phonology in word reading.


The database we used does not include this information, but it would be useful for customers and is an attribute of real properties.


Bearing this in mind, we attempted to annotate each tweet not only based on the presence or absence of HS, but also on other parameters that may even increase, or rather mitigate, the impact of the message.

we came up with a set of annotation categories and guidelines that attempt to encompass all those variables in a single coherent framework.


"), the multiple ways in which it can be conveyed, as well as our choice to use only neutral keywords -rather than more explicit termsto filter the corpus, somehow prevent the selection of precise lexical patterns in the identification of HS, as well as of the other categories.

also recalling what stated at the beginning of Section 4., the selection of the tags to be associated with each tweet is determined case by case, based on its very content, on the context it refers to (whenever such information can be extrapolated from the text), and on the general principles indicated in the guidelines.


The interests dataset can be extended to more languages and domains through the same methodology.

the dataset and its extensions can be used in a number of applications in the domain of Recommender Systems, but not only.


The posts and responses in question are all public; however, to protect the anonymity of Facebook users in our dataset we remove all identifying user information as well as Facebook-internal information such as User IDs and Post IDs, replacing these with randomized ID numbers.

users whose comments appear multiple times in our dataset may be compared, but without revealing their identity.


As discussed in the introduction, high performing NLP's neural models often require huge volumes of annotated data to produce powerful models and prevent over-fitting.

in the case of social media content, it is difficult to achieve the performances of state-of-the-art models based on hand-crafted features by applying neural models trained on small amounts of annotated data.


(Meftah et al., 2017) showed that an initialization with a combination of several pre-trained embedding vectors (from different pre-trained models) improves significantly the performances.

for English experiments, we initialize word embedding with a concatenation of four pre-trained models: 1.


Moreover, if the speaker is supposed to record in English which is not his/her native language, practical difficulties and expenses increase even more.

we have decided to utilize a domain-specific approach to TTS voice creation, relying on the fact that the natural language generating module of the artificial pseudopilot generates utterances only from the restricted domain of typical ATC radio communication (albeit the number of potential different utterances is unlimited).


However, a relatively high number of problematic or even unintelligible synthetic speech segments (words) were observed.

more formal listening tests were carried out, with two independent listeners evaluating 500 testing sentences from the held-out data of the source sentence database.


Although it is possible to search for these data from the website of each data center, the metadata of the corpus descriptions are not unified and also we cannot specify speech-specific conditions such as the recording environment in the search, so it is not easy for users to find the necessary corpus.

it would be more convenient for corpus users if the catalogue specifications of the corpora were standardized among all the various data centers and if the speech corpora could be retrieved by utilizing speech-specific conditions.


However, SHACHI mainly focuses on text corpora and does not have sufficient suitable attributes to describe speech corpora.

we have revised the set of attributes and items.


Also, it was not easy to grasp the number of retrieved corpora.

we have devised a new system that can display all the retrieved corpora in a single page by reducing the size of each corpus icon.


morpho-syntax) with the aim to combine and compare multiple layers in order to derive confidence estimations for the annotations.

parts of the data where the output of several tools match can be considered clear-cut cases, while mismatches hint at areas of interest which are potentially challenging or where rare phenomena can be found.


The goal of that project is to prove the effectiveness of a specific therapy method.

patients are aware that they are being recorded and that the material is used for research.


These features are based on the literature presented in Section 2. above and on the guidelines by the psychotherapists (see Section 3.).

we focus on elements such as pauses, social noise, disfluencies, but also on the vocabulary used.


This metric does not account for all possible boundaries in the corpus: theoretically each turn could be split at any space between words.

the metric is quite pessimistic but gives more insight into annotators performance.


In total, 33,683 workers participated in this task.

feature changes of 9,073 event sentences (types) were acquired (including 975 verbs (types) and 19,052 arguments (tokens) (4,882 types), 5,647 case frames (types)).


After the task, for each trigger utterance, overlapping replies and extremely low-quality replies (such as copied and pasted trigger utterances and empty replies) were discarded.

23,196 replies (2,428 types of trigger utterances) were acquired.


In total, 5,605 workers participated in this task, and ten workers were assigned to each dialogue.

judgements on the reasonableness of the 23,196 dialogues were acquired.


We are planning to start with applying feature change information to understanding of simple texts.

we made artificial trigger utterances based on event sentences in JFCKB that are based on highly frequent words in various Web documents.


Each feature change vector of each case is composed of a triple (increased, decreased, and unchanged) whose values are probabilities.

in this study, the dimension of predicate feature change vectors is 96.


The difference between these models was that whether the emotional change information was used.

of the experiment, the model using the emotional change information outperformed the other.


Information navigation and attentive listening are tasks in a good position between them; goals of these tasks are more ambiguous than task-oriented dialogue, but tasks or domains can be limited in the task definition.

we focus on collecting dialogue corpora of information navigation and attentive listening in this paper.


The country was the second largest consumer of antibiotics in the world during 2000-2010 (Boeckel et al., 2014) , and its bacterial resistance has reached alarming levels (Xiao et al., 2012).

antibiotic overuse is an extremely severe and urgent problem in China.


The label of each dialogue act was expressed as a binary value as to whether the dialogue act appeared.

the feature value was a 17-dimensional vector.


At a lexical level, the transcripts are are a rich source of information that may reveal vocabulary variation (type/token ratio) of speakers, lexical or syntactic patterns they may use, as well as a set of markers that are related to politeness, engagement and co-operation.

the dataset is profiled according to the aforementioned cues and can be revisited accordingly at various levels and signal-or natural language processing tasks.


• Agglutinating languages also have a variety of grammatical morphemes, but each of them has no more than one feature, and more than two morphemes can be concatenated to a stem.

1 the set of possible word forms of a stem is larger than in inflected languages.


The algorithm may now terminate or may be called recursively to describe o4 as well, resulting in a description that could be subsequently realised as in, e.g., 'the cone near a/the large box'.

attribute selection is usually driven by discriminatory power (Olson, 1970) (e.g., in the above example, only properties that rule out at least one distractor object are selected), and it is heavily influenced by the order P in which attributes are considered for inclusion in L. output descriptions may vary considerable both in length and in the kinds of information that they convey (Paraboni, 2003).


Moreover, even assuming that all speakers are sufficiently careful during the experiment, situations of communication that are deemed less critical or somewhat less important may still affect attribute choice and referential overspecification (Arts et al., 2011), issues that are at the heart of many REG studies.

monologue and dialogue situations of communication may not be entirely comparable or, at the very least, may elicit different referring expressions.


However, studies of this kind will often rely on large collections of pre-recorded linguistic examples produced by every single speaker of interest, and on every domain under consideration, to obtain meaning-to-text mappings from which the lexicalisation model is built.

speaker-dependent lexicalisation may be impractical when suitable annotated corpora are not available.


Corpus-based studies of this kind, however, will usually rely on a large collection of pre-recorded linguistic examples produced by every single speaker of interest, and on every domain of interest.

corpus-based lexicalisation may not always be a viable solution for practical NLG applications.


Given our goal of learning alternative lexicalisations for a given input property, the trivial cases represented by properties with a single possible lexicalisation in the corpus were disregarded.

of the annotation task, a set of 4,345 propertyword mappings was obtained.


Finally, the left attribute is considered.

since neither R1 or R7 are on the left side of anything sufficiently close for the purpose of reference (and therefore do not have a left property defined in the knowledge base), (left-R6) is also added to L, and both distractors are removed from C. the context C is now empty, and the algorithm terminates by returning the expression L = {(type, monster), (left, R6)}, in which case R6 itself could be described recursively as {(type, cactus)}.


A series of 23 individual trials was performed upon appointment with each participant.

a set of 368 descriptions of interest was obtained.


A thorough human evaluation performed on over 3,000 sentence pairs showed that the accuracy of automatic alignment between the sentences from two neighboring Newsela levels of simplification is between 83% (for levels 3 and 4) and 98% (for levels 0 and 1), while the alignment method aligns sentences from the hardest and the easiest levels (0 and 4), with only 58% accuracy (Štajner et al., 2017).

we aligned and used only the neighboring Newsela levels (0-1, 1-2, 2-3, and 3-4) as training data.


6b Dress-LS As a result, although many mosques will not enforce violations, both men and women.

6c NTS-SARI-cross many mosques will not enforce violations, both men and women when attending a mosque must follow these guidelines.


Additionally, a considerable number of NLG approaches can be found to European or Brazilian Portuguese languages (Pereira and Paraboni, 2008;Cuevas and Paraboni, 2008;de Novais et al., 2009;de Novais et al., 2010;de Novais et al., 2012;de Novais and Paraboni, 2013;De Oliveira and Sripada, 2014;Pereira et al., 2015), however, none of them have exploited the generation of NL from RDF.

to the best of our knowledge, RDF2PT is the first proposed approach to this end.


If the subject is a woman, we only refer to it by using the article "a", i.e., "a gerente".

there are some challenges to tackle for recognizing the gender and assigning it correctly.


For example, the predicate dbo:knownFor, which Portuguese label is "conhecido por", has the first word identified as an inflection of the verb "conhecer" (en:know).

rDF2PT lexicalizes and sets it as a verb.


The possessive pronoun seu in this sentence does not indicate explicitly to whom the dictionary belongs, if it belongs to the professora (eng:teacher) or aluno (eng:student).

we have explicitly to define the possessive pronoun in order to decrease the ambiguity in texts and it is obviously important when generating text from data.


This difference was crucial in the perspective of our evaluators.

the generation of subordinate sentences in Portuguese must be investigated in the near future.


Early work applying traditional semantic role labeling (SRL) techniques to iSRL was met with deflating results.

researchers limited themselves to simplistic machine learning models such as Naïve Bayes (Feizabadi and Pado, 2015, among others) or abandoned machine learning altogether (Laparra and Rigau, 2013).


The authors chose five highly frequent verbs to annotate in order to create "high-volume of annotations for specific verb predicates".

1 the words and phrases that fill each role are very diverse, as illustrated by the examples in Table 2.


The dictionary component of the DWDS is based on high-coverage and detailed dictionar-1 https://www.dwds.de/ ies of contemporary German, including the Wörterbuch der deutschen Gegenwartssprache and Duden Großwörterbuch der deutschen Sprache.

the DWDS dictionary component with more than 170,000 entries provides a good coverage of the core vocabulary of German.